Overview

Dataset statistics

Number of variables80
Number of observations2919
Missing cells17166
Missing cells (%)7.4%
Total size in memory1.8 MiB
Average record size in memory648.0 B

Variable types

Numeric37
Text43

Alerts

LotFrontage has 486 (16.6%) missing valuesMissing
Alley has 2721 (93.2%) missing valuesMissing
MasVnrType has 1766 (60.5%) missing valuesMissing
BsmtQual has 81 (2.8%) missing valuesMissing
BsmtCond has 82 (2.8%) missing valuesMissing
BsmtExposure has 82 (2.8%) missing valuesMissing
BsmtFinType1 has 79 (2.7%) missing valuesMissing
BsmtFinType2 has 80 (2.7%) missing valuesMissing
FireplaceQu has 1420 (48.6%) missing valuesMissing
GarageType has 157 (5.4%) missing valuesMissing
GarageYrBlt has 159 (5.4%) missing valuesMissing
GarageFinish has 159 (5.4%) missing valuesMissing
GarageQual has 159 (5.4%) missing valuesMissing
GarageCond has 159 (5.4%) missing valuesMissing
PoolQC has 2909 (99.7%) missing valuesMissing
Fence has 2348 (80.4%) missing valuesMissing
MiscFeature has 2814 (96.4%) missing valuesMissing
SalePrice has 1459 (50.0%) missing valuesMissing
MiscVal is highly skewed (γ1 = 21.95848032)Skewed
MasVnrArea has 1738 (59.5%) zerosZeros
BsmtFinSF1 has 929 (31.8%) zerosZeros
BsmtFinSF2 has 2571 (88.1%) zerosZeros
BsmtUnfSF has 241 (8.3%) zerosZeros
TotalBsmtSF has 78 (2.7%) zerosZeros
2ndFlrSF has 1668 (57.1%) zerosZeros
LowQualFinSF has 2879 (98.6%) zerosZeros
BsmtFullBath has 1705 (58.4%) zerosZeros
BsmtHalfBath has 2742 (93.9%) zerosZeros
HalfBath has 1834 (62.8%) zerosZeros
Fireplaces has 1420 (48.6%) zerosZeros
GarageCars has 157 (5.4%) zerosZeros
GarageArea has 157 (5.4%) zerosZeros
WoodDeckSF has 1523 (52.2%) zerosZeros
OpenPorchSF has 1298 (44.5%) zerosZeros
EnclosedPorch has 2460 (84.3%) zerosZeros
3SsnPorch has 2882 (98.7%) zerosZeros
ScreenPorch has 2663 (91.2%) zerosZeros
PoolArea has 2906 (99.6%) zerosZeros
MiscVal has 2816 (96.5%) zerosZeros

Reproduction

Analysis started2024-10-24 05:28:20.474153
Analysis finished2024-10-24 05:28:21.355400
Duration0.88 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

MSSubClass
Real number (ℝ)

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.1377184
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:21.426402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.51762783
Coefficient of variation (CV)0.7441254048
Kurtosis1.457827477
Mean57.1377184
Median Absolute Deviation (MAD)30
Skewness1.376164637
Sum166785
Variance1807.748676
MonotonicityNot monotonic
2024-10-24T14:28:21.542422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20 1079
37.0%
60 575
19.7%
50 287
 
9.8%
120 182
 
6.2%
30 139
 
4.8%
70 128
 
4.4%
160 128
 
4.4%
80 118
 
4.0%
90 109
 
3.7%
190 61
 
2.1%
Other values (6) 113
 
3.9%
ValueCountFrequency (%)
20 1079
37.0%
30 139
 
4.8%
40 6
 
0.2%
45 18
 
0.6%
50 287
 
9.8%
ValueCountFrequency (%)
190 61
 
2.1%
180 17
 
0.6%
160 128
4.4%
150 1
 
< 0.1%
120 182
6.2%
Distinct5
Distinct (%)0.2%
Missing4
Missing (%)0.1%
Memory size45.6 KiB
2024-10-24T14:28:21.629324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.042881647
Min length2

Characters and Unicode

Total characters5955
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL
ValueCountFrequency (%)
rl 2265
77.0%
rm 460
 
15.6%
fv 139
 
4.7%
rh 26
 
0.9%
c 25
 
0.9%
all 25
 
0.9%
2024-10-24T14:28:21.846495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 2751
46.2%
L 2265
38.0%
M 460
 
7.7%
F 139
 
2.3%
V 139
 
2.3%
l 50
 
0.8%
H 26
 
0.4%
C 25
 
0.4%
25
 
0.4%
( 25
 
0.4%
Other values (2) 50
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 2751
46.2%
L 2265
38.0%
M 460
 
7.7%
F 139
 
2.3%
V 139
 
2.3%
l 50
 
0.8%
H 26
 
0.4%
C 25
 
0.4%
25
 
0.4%
( 25
 
0.4%
Other values (2) 50
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 2751
46.2%
L 2265
38.0%
M 460
 
7.7%
F 139
 
2.3%
V 139
 
2.3%
l 50
 
0.8%
H 26
 
0.4%
C 25
 
0.4%
25
 
0.4%
( 25
 
0.4%
Other values (2) 50
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 2751
46.2%
L 2265
38.0%
M 460
 
7.7%
F 139
 
2.3%
V 139
 
2.3%
l 50
 
0.8%
H 26
 
0.4%
C 25
 
0.4%
25
 
0.4%
( 25
 
0.4%
Other values (2) 50
 
0.8%

LotFrontage
Real number (ℝ)

MISSING 

Distinct128
Distinct (%)5.3%
Missing486
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean69.30579531
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:21.992493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile32
Q159
median68
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation23.34490471
Coefficient of variation (CV)0.3368391431
Kurtosis11.29592065
Mean69.30579531
Median Absolute Deviation (MAD)12
Skewness1.503277815
Sum168621
Variance544.9845758
MonotonicityNot monotonic
2024-10-24T14:28:22.135396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 276
 
9.5%
80 137
 
4.7%
70 133
 
4.6%
50 117
 
4.0%
75 105
 
3.6%
65 93
 
3.2%
85 76
 
2.6%
21 50
 
1.7%
24 49
 
1.7%
63 47
 
1.6%
Other values (118) 1350
46.2%
(Missing) 486
 
16.6%
ValueCountFrequency (%)
21 50
1.7%
22 1
 
< 0.1%
24 49
1.7%
25 1
 
< 0.1%
26 3
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
200 1
< 0.1%
195 1
< 0.1%
182 1
< 0.1%
174 2
0.1%

LotArea
Real number (ℝ)

Distinct1951
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10168.11408
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:22.281636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3182
Q17478
median9453
Q311570
95-th percentile17142.9
Maximum215245
Range213945
Interquartile range (IQR)4092

Descriptive statistics

Standard deviation7886.996359
Coefficient of variation (CV)0.7756597041
Kurtosis264.9523101
Mean10168.11408
Median Absolute Deviation (MAD)2039
Skewness12.82902485
Sum29680725
Variance62204711.57
MonotonicityNot monotonic
2024-10-24T14:28:23.130296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9600 44
 
1.5%
7200 43
 
1.5%
6000 34
 
1.2%
9000 29
 
1.0%
10800 25
 
0.9%
7500 21
 
0.7%
8400 21
 
0.7%
6240 18
 
0.6%
1680 18
 
0.6%
6120 17
 
0.6%
Other values (1941) 2649
90.8%
ValueCountFrequency (%)
1300 1
< 0.1%
1470 1
< 0.1%
1476 1
< 0.1%
1477 2
0.1%
1484 1
< 0.1%
ValueCountFrequency (%)
215245 1
< 0.1%
164660 1
< 0.1%
159000 1
< 0.1%
115149 1
< 0.1%
70761 1
< 0.1%

Street
Text

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:23.232311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters11676
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave
ValueCountFrequency (%)
pave 2907
99.6%
grvl 12
 
0.4%
2024-10-24T14:28:23.445010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
v 2919
25.0%
P 2907
24.9%
a 2907
24.9%
e 2907
24.9%
G 12
 
0.1%
r 12
 
0.1%
l 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v 2919
25.0%
P 2907
24.9%
a 2907
24.9%
e 2907
24.9%
G 12
 
0.1%
r 12
 
0.1%
l 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v 2919
25.0%
P 2907
24.9%
a 2907
24.9%
e 2907
24.9%
G 12
 
0.1%
r 12
 
0.1%
l 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v 2919
25.0%
P 2907
24.9%
a 2907
24.9%
e 2907
24.9%
G 12
 
0.1%
r 12
 
0.1%
l 12
 
0.1%

Alley
Text

MISSING 

Distinct2
Distinct (%)1.0%
Missing2721
Missing (%)93.2%
Memory size45.6 KiB
2024-10-24T14:28:23.552782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters792
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrvl
2nd rowPave
3rd rowPave
4th rowGrvl
5th rowPave
ValueCountFrequency (%)
grvl 120
60.6%
pave 78
39.4%
2024-10-24T14:28:23.769132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
v 198
25.0%
G 120
15.2%
r 120
15.2%
l 120
15.2%
P 78
 
9.8%
a 78
 
9.8%
e 78
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v 198
25.0%
G 120
15.2%
r 120
15.2%
l 120
15.2%
P 78
 
9.8%
a 78
 
9.8%
e 78
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v 198
25.0%
G 120
15.2%
r 120
15.2%
l 120
15.2%
P 78
 
9.8%
a 78
 
9.8%
e 78
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v 198
25.0%
G 120
15.2%
r 120
15.2%
l 120
15.2%
P 78
 
9.8%
a 78
 
9.8%
e 78
 
9.8%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:23.875179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8757
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1
ValueCountFrequency (%)
reg 1859
63.7%
ir1 968
33.2%
ir2 76
 
2.6%
ir3 16
 
0.5%
2024-10-24T14:28:24.092190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 2919
33.3%
e 1859
21.2%
g 1859
21.2%
I 1060
 
12.1%
1 968
 
11.1%
2 76
 
0.9%
3 16
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 2919
33.3%
e 1859
21.2%
g 1859
21.2%
I 1060
 
12.1%
1 968
 
11.1%
2 76
 
0.9%
3 16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 2919
33.3%
e 1859
21.2%
g 1859
21.2%
I 1060
 
12.1%
1 968
 
11.1%
2 76
 
0.9%
3 16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 2919
33.3%
e 1859
21.2%
g 1859
21.2%
I 1060
 
12.1%
1 968
 
11.1%
2 76
 
0.9%
3 16
 
0.2%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:24.191162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8757
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl
ValueCountFrequency (%)
lvl 2622
89.8%
hls 120
 
4.1%
bnk 117
 
4.0%
low 60
 
2.1%
2024-10-24T14:28:24.404899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 2802
32.0%
v 2622
29.9%
l 2622
29.9%
H 120
 
1.4%
S 120
 
1.4%
B 117
 
1.3%
n 117
 
1.3%
k 117
 
1.3%
o 60
 
0.7%
w 60
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 2802
32.0%
v 2622
29.9%
l 2622
29.9%
H 120
 
1.4%
S 120
 
1.4%
B 117
 
1.3%
n 117
 
1.3%
k 117
 
1.3%
o 60
 
0.7%
w 60
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 2802
32.0%
v 2622
29.9%
l 2622
29.9%
H 120
 
1.4%
S 120
 
1.4%
B 117
 
1.3%
n 117
 
1.3%
k 117
 
1.3%
o 60
 
0.7%
w 60
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 2802
32.0%
v 2622
29.9%
l 2622
29.9%
H 120
 
1.4%
S 120
 
1.4%
B 117
 
1.3%
n 117
 
1.3%
k 117
 
1.3%
o 60
 
0.7%
w 60
 
0.7%
Distinct2
Distinct (%)0.1%
Missing2
Missing (%)0.1%
Memory size45.6 KiB
2024-10-24T14:28:24.519065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters17502
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub
ValueCountFrequency (%)
allpub 2916
> 99.9%
nosewa 1
 
< 0.1%
2024-10-24T14:28:24.745559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 5832
33.3%
A 2916
16.7%
P 2916
16.7%
u 2916
16.7%
b 2916
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 5832
33.3%
A 2916
16.7%
P 2916
16.7%
u 2916
16.7%
b 2916
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 5832
33.3%
A 2916
16.7%
P 2916
16.7%
u 2916
16.7%
b 2916
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 5832
33.3%
A 2916
16.7%
P 2916
16.7%
u 2916
16.7%
b 2916
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%
Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:24.863687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.958547448
Min length3

Characters and Unicode

Total characters17393
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2
ValueCountFrequency (%)
inside 2133
73.1%
corner 511
 
17.5%
culdsac 176
 
6.0%
fr2 85
 
2.9%
fr3 14
 
0.5%
2024-10-24T14:28:25.126848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2644
15.2%
n 2644
15.2%
I 2133
12.3%
s 2133
12.3%
i 2133
12.3%
d 2133
12.3%
r 1022
 
5.9%
C 687
 
3.9%
o 511
 
2.9%
S 176
 
1.0%
Other values (9) 1177
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17393
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2644
15.2%
n 2644
15.2%
I 2133
12.3%
s 2133
12.3%
i 2133
12.3%
d 2133
12.3%
r 1022
 
5.9%
C 687
 
3.9%
o 511
 
2.9%
S 176
 
1.0%
Other values (9) 1177
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17393
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2644
15.2%
n 2644
15.2%
I 2133
12.3%
s 2133
12.3%
i 2133
12.3%
d 2133
12.3%
r 1022
 
5.9%
C 687
 
3.9%
o 511
 
2.9%
S 176
 
1.0%
Other values (9) 1177
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17393
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2644
15.2%
n 2644
15.2%
I 2133
12.3%
s 2133
12.3%
i 2133
12.3%
d 2133
12.3%
r 1022
 
5.9%
C 687
 
3.9%
o 511
 
2.9%
S 176
 
1.0%
Other values (9) 1177
6.8%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:25.222026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8757
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl
ValueCountFrequency (%)
gtl 2778
95.2%
mod 125
 
4.3%
sev 16
 
0.5%
2024-10-24T14:28:25.430676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 2778
31.7%
t 2778
31.7%
l 2778
31.7%
M 125
 
1.4%
o 125
 
1.4%
d 125
 
1.4%
S 16
 
0.2%
e 16
 
0.2%
v 16
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 2778
31.7%
t 2778
31.7%
l 2778
31.7%
M 125
 
1.4%
o 125
 
1.4%
d 125
 
1.4%
S 16
 
0.2%
e 16
 
0.2%
v 16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 2778
31.7%
t 2778
31.7%
l 2778
31.7%
M 125
 
1.4%
o 125
 
1.4%
d 125
 
1.4%
S 16
 
0.2%
e 16
 
0.2%
v 16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 2778
31.7%
t 2778
31.7%
l 2778
31.7%
M 125
 
1.4%
o 125
 
1.4%
d 125
 
1.4%
S 16
 
0.2%
e 16
 
0.2%
v 16
 
0.2%
Distinct25
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:25.592241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.500171292
Min length5

Characters and Unicode

Total characters18974
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge
ValueCountFrequency (%)
names 443
15.2%
collgcr 267
 
9.1%
oldtown 239
 
8.2%
edwards 194
 
6.6%
somerst 182
 
6.2%
nridght 166
 
5.7%
gilbert 165
 
5.7%
sawyer 151
 
5.2%
nwames 131
 
4.5%
sawyerw 125
 
4.3%
Other values (15) 856
29.3%
2024-10-24T14:28:25.894043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 1829
 
9.6%
e 1816
 
9.6%
l 1210
 
6.4%
d 1009
 
5.3%
s 960
 
5.1%
o 950
 
5.0%
m 856
 
4.5%
w 849
 
4.5%
N 834
 
4.4%
C 725
 
3.8%
Other values (28) 7936
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1829
 
9.6%
e 1816
 
9.6%
l 1210
 
6.4%
d 1009
 
5.3%
s 960
 
5.1%
o 950
 
5.0%
m 856
 
4.5%
w 849
 
4.5%
N 834
 
4.4%
C 725
 
3.8%
Other values (28) 7936
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1829
 
9.6%
e 1816
 
9.6%
l 1210
 
6.4%
d 1009
 
5.3%
s 960
 
5.1%
o 950
 
5.0%
m 856
 
4.5%
w 849
 
4.5%
N 834
 
4.4%
C 725
 
3.8%
Other values (28) 7936
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1829
 
9.6%
e 1816
 
9.6%
l 1210
 
6.4%
d 1009
 
5.3%
s 960
 
5.1%
o 950
 
5.0%
m 856
 
4.5%
w 849
 
4.5%
N 834
 
4.4%
C 725
 
3.8%
Other values (28) 7936
41.8%
Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:26.018865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.119218911
Min length4

Characters and Unicode

Total characters12024
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm
ValueCountFrequency (%)
norm 2511
86.0%
feedr 164
 
5.6%
artery 92
 
3.2%
rran 50
 
1.7%
posn 39
 
1.3%
rrae 28
 
1.0%
posa 20
 
0.7%
rrnn 9
 
0.3%
rrne 6
 
0.2%
2024-10-24T14:28:26.270018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 2859
23.8%
o 2570
21.4%
N 2565
21.3%
m 2511
20.9%
e 454
 
3.8%
A 190
 
1.6%
R 186
 
1.5%
F 164
 
1.4%
d 164
 
1.4%
t 92
 
0.8%
Other values (4) 269
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2859
23.8%
o 2570
21.4%
N 2565
21.3%
m 2511
20.9%
e 454
 
3.8%
A 190
 
1.6%
R 186
 
1.5%
F 164
 
1.4%
d 164
 
1.4%
t 92
 
0.8%
Other values (4) 269
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2859
23.8%
o 2570
21.4%
N 2565
21.3%
m 2511
20.9%
e 454
 
3.8%
A 190
 
1.6%
R 186
 
1.5%
F 164
 
1.4%
d 164
 
1.4%
t 92
 
0.8%
Other values (4) 269
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2859
23.8%
o 2570
21.4%
N 2565
21.3%
m 2511
20.9%
e 454
 
3.8%
A 190
 
1.6%
R 186
 
1.5%
F 164
 
1.4%
d 164
 
1.4%
t 92
 
0.8%
Other values (4) 269
 
2.2%
Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:26.393705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.007879411
Min length4

Characters and Unicode

Total characters11699
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm
ValueCountFrequency (%)
norm 2889
99.0%
feedr 13
 
0.4%
artery 5
 
0.2%
posn 4
 
0.1%
posa 4
 
0.1%
rrnn 2
 
0.1%
rran 1
 
< 0.1%
rrae 1
 
< 0.1%
2024-10-24T14:28:26.643023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 2912
24.9%
o 2897
24.8%
N 2895
24.7%
m 2889
24.7%
e 32
 
0.3%
F 13
 
0.1%
d 13
 
0.1%
A 11
 
0.1%
P 8
 
0.1%
s 8
 
0.1%
Other values (4) 21
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11699
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2912
24.9%
o 2897
24.8%
N 2895
24.7%
m 2889
24.7%
e 32
 
0.3%
F 13
 
0.1%
d 13
 
0.1%
A 11
 
0.1%
P 8
 
0.1%
s 8
 
0.1%
Other values (4) 21
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11699
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2912
24.9%
o 2897
24.8%
N 2895
24.7%
m 2889
24.7%
e 32
 
0.3%
F 13
 
0.1%
d 13
 
0.1%
A 11
 
0.1%
P 8
 
0.1%
s 8
 
0.1%
Other values (4) 21
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11699
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2912
24.9%
o 2897
24.8%
N 2895
24.7%
m 2889
24.7%
e 32
 
0.3%
F 13
 
0.1%
d 13
 
0.1%
A 11
 
0.1%
P 8
 
0.1%
s 8
 
0.1%
Other values (4) 21
 
0.2%
Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:26.754343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.305584104
Min length4

Characters and Unicode

Total characters12568
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam
ValueCountFrequency (%)
1fam 2425
83.1%
twnhse 227
 
7.8%
duplex 109
 
3.7%
twnhs 96
 
3.3%
2fmcon 62
 
2.1%
2024-10-24T14:28:27.030973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 2487
19.8%
1 2425
19.3%
a 2425
19.3%
F 2425
19.3%
n 385
 
3.1%
T 323
 
2.6%
w 323
 
2.6%
h 323
 
2.6%
s 323
 
2.6%
E 227
 
1.8%
Other values (10) 902
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 2487
19.8%
1 2425
19.3%
a 2425
19.3%
F 2425
19.3%
n 385
 
3.1%
T 323
 
2.6%
w 323
 
2.6%
h 323
 
2.6%
s 323
 
2.6%
E 227
 
1.8%
Other values (10) 902
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 2487
19.8%
1 2425
19.3%
a 2425
19.3%
F 2425
19.3%
n 385
 
3.1%
T 323
 
2.6%
w 323
 
2.6%
h 323
 
2.6%
s 323
 
2.6%
E 227
 
1.8%
Other values (10) 902
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 2487
19.8%
1 2425
19.3%
a 2425
19.3%
F 2425
19.3%
n 385
 
3.1%
T 323
 
2.6%
w 323
 
2.6%
h 323
 
2.6%
s 323
 
2.6%
E 227
 
1.8%
Other values (10) 902
 
7.2%
Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:27.173191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.912298732
Min length4

Characters and Unicode

Total characters17258
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story
ValueCountFrequency (%)
1story 1471
50.4%
2story 872
29.9%
1.5fin 314
 
10.8%
slvl 128
 
4.4%
sfoyer 83
 
2.8%
2.5unf 24
 
0.8%
1.5unf 19
 
0.7%
2.5fin 8
 
0.3%
2024-10-24T14:28:27.472830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 2554
14.8%
o 2426
14.1%
r 2426
14.1%
y 2426
14.1%
t 2343
13.6%
1 1804
10.5%
2 904
 
5.2%
F 405
 
2.3%
5 365
 
2.1%
. 365
 
2.1%
Other values (8) 1240
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2554
14.8%
o 2426
14.1%
r 2426
14.1%
y 2426
14.1%
t 2343
13.6%
1 1804
10.5%
2 904
 
5.2%
F 405
 
2.3%
5 365
 
2.1%
. 365
 
2.1%
Other values (8) 1240
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2554
14.8%
o 2426
14.1%
r 2426
14.1%
y 2426
14.1%
t 2343
13.6%
1 1804
10.5%
2 904
 
5.2%
F 405
 
2.3%
5 365
 
2.1%
. 365
 
2.1%
Other values (8) 1240
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2554
14.8%
o 2426
14.1%
r 2426
14.1%
y 2426
14.1%
t 2343
13.6%
1 1804
10.5%
2 904
 
5.2%
F 405
 
2.3%
5 365
 
2.1%
. 365
 
2.1%
Other values (8) 1240
7.2%

OverallQual
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0890716
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:27.597190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.409947207
Coefficient of variation (CV)0.2315537243
Kurtosis0.06721935991
Mean6.0890716
Median Absolute Deviation (MAD)1
Skewness0.1972118053
Sum17774
Variance1.987951125
MonotonicityNot monotonic
2024-10-24T14:28:27.694526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 825
28.3%
6 731
25.0%
7 600
20.6%
8 342
11.7%
4 226
 
7.7%
9 107
 
3.7%
3 40
 
1.4%
10 31
 
1.1%
2 13
 
0.4%
1 4
 
0.1%
ValueCountFrequency (%)
1 4
 
0.1%
2 13
 
0.4%
3 40
 
1.4%
4 226
 
7.7%
5 825
28.3%
ValueCountFrequency (%)
10 31
 
1.1%
9 107
 
3.7%
8 342
11.7%
7 600
20.6%
6 731
25.0%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.56457691
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:27.791152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.113130747
Coefficient of variation (CV)0.2000387028
Kurtosis1.479447334
Mean5.56457691
Median Absolute Deviation (MAD)0
Skewness0.5706053117
Sum16243
Variance1.239060059
MonotonicityNot monotonic
2024-10-24T14:28:27.902257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 1645
56.4%
6 531
 
18.2%
7 390
 
13.4%
8 144
 
4.9%
4 101
 
3.5%
3 50
 
1.7%
9 41
 
1.4%
2 10
 
0.3%
1 7
 
0.2%
ValueCountFrequency (%)
1 7
 
0.2%
2 10
 
0.3%
3 50
 
1.7%
4 101
 
3.5%
5 1645
56.4%
ValueCountFrequency (%)
9 41
 
1.4%
8 144
 
4.9%
7 390
 
13.4%
6 531
 
18.2%
5 1645
56.4%

YearBuilt
Real number (ℝ)

Distinct118
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.312778
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:28.031111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1915
Q11953.5
median1973
Q32001
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation30.29144153
Coefficient of variation (CV)0.01536612651
Kurtosis-0.5113172971
Mean1971.312778
Median Absolute Deviation (MAD)25
Skewness-0.6001139749
Sum5754262
Variance917.5714302
MonotonicityNot monotonic
2024-10-24T14:28:28.163043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 142
 
4.9%
2006 138
 
4.7%
2007 109
 
3.7%
2004 99
 
3.4%
2003 88
 
3.0%
1977 57
 
2.0%
1920 57
 
2.0%
1976 54
 
1.8%
1999 52
 
1.8%
2008 49
 
1.7%
Other values (108) 2074
71.1%
ValueCountFrequency (%)
1872 1
 
< 0.1%
1875 1
 
< 0.1%
1879 1
 
< 0.1%
1880 5
0.2%
1882 1
 
< 0.1%
ValueCountFrequency (%)
2010 3
 
0.1%
2009 25
 
0.9%
2008 49
 
1.7%
2007 109
3.7%
2006 138
4.7%

YearRemodAdd
Real number (ℝ)

Distinct61
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.264474
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:28.297982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11965
median1993
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)39

Descriptive statistics

Standard deviation20.89434423
Coefficient of variation (CV)0.01053001982
Kurtosis-1.346431392
Mean1984.264474
Median Absolute Deviation (MAD)14
Skewness-0.4512522973
Sum5792068
Variance436.573621
MonotonicityNot monotonic
2024-10-24T14:28:28.442379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 361
 
12.4%
2006 202
 
6.9%
2007 164
 
5.6%
2005 141
 
4.8%
2004 111
 
3.8%
2000 104
 
3.6%
2003 99
 
3.4%
2002 82
 
2.8%
2008 81
 
2.8%
1998 77
 
2.6%
Other values (51) 1497
51.3%
ValueCountFrequency (%)
1950 361
12.4%
1951 14
 
0.5%
1952 15
 
0.5%
1953 20
 
0.7%
1954 28
 
1.0%
ValueCountFrequency (%)
2010 13
 
0.4%
2009 34
 
1.2%
2008 81
2.8%
2007 164
5.6%
2006 202
6.9%
Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:28.563191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.636519356
Min length3

Characters and Unicode

Total characters13534
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable
ValueCountFrequency (%)
gable 2310
79.1%
hip 551
 
18.9%
gambrel 22
 
0.8%
flat 20
 
0.7%
mansard 11
 
0.4%
shed 5
 
0.2%
2024-10-24T14:28:29.042301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2374
17.5%
l 2352
17.4%
e 2337
17.3%
G 2332
17.2%
b 2332
17.2%
H 551
 
4.1%
i 551
 
4.1%
p 551
 
4.1%
r 33
 
0.2%
m 22
 
0.2%
Other values (8) 99
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13534
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2374
17.5%
l 2352
17.4%
e 2337
17.3%
G 2332
17.2%
b 2332
17.2%
H 551
 
4.1%
i 551
 
4.1%
p 551
 
4.1%
r 33
 
0.2%
m 22
 
0.2%
Other values (8) 99
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13534
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2374
17.5%
l 2352
17.4%
e 2337
17.3%
G 2332
17.2%
b 2332
17.2%
H 551
 
4.1%
i 551
 
4.1%
p 551
 
4.1%
r 33
 
0.2%
m 22
 
0.2%
Other values (8) 99
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13534
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2374
17.5%
l 2352
17.4%
e 2337
17.3%
G 2332
17.2%
b 2332
17.2%
H 551
 
4.1%
i 551
 
4.1%
p 551
 
4.1%
r 33
 
0.2%
m 22
 
0.2%
Other values (8) 99
 
0.7%
Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:29.175619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.998287085
Min length4

Characters and Unicode

Total characters20428
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg
ValueCountFrequency (%)
compshg 2876
98.5%
tar&grv 23
 
0.8%
wdshake 9
 
0.3%
wdshngl 7
 
0.2%
metal 1
 
< 0.1%
membran 1
 
< 0.1%
roll 1
 
< 0.1%
clytile 1
 
< 0.1%
2024-10-24T14:28:29.432324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 2892
14.2%
h 2892
14.2%
g 2883
14.1%
C 2877
14.1%
m 2877
14.1%
o 2877
14.1%
p 2876
14.1%
r 47
 
0.2%
a 34
 
0.2%
T 24
 
0.1%
Other values (15) 149
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2892
14.2%
h 2892
14.2%
g 2883
14.1%
C 2877
14.1%
m 2877
14.1%
o 2877
14.1%
p 2876
14.1%
r 47
 
0.2%
a 34
 
0.2%
T 24
 
0.1%
Other values (15) 149
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2892
14.2%
h 2892
14.2%
g 2883
14.1%
C 2877
14.1%
m 2877
14.1%
o 2877
14.1%
p 2876
14.1%
r 47
 
0.2%
a 34
 
0.2%
T 24
 
0.1%
Other values (15) 149
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2892
14.2%
h 2892
14.2%
g 2883
14.1%
C 2877
14.1%
m 2877
14.1%
o 2877
14.1%
p 2876
14.1%
r 47
 
0.2%
a 34
 
0.2%
T 24
 
0.1%
Other values (15) 149
 
0.7%
Distinct15
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Memory size45.6 KiB
2024-10-24T14:28:29.577176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.983207676
Min length5

Characters and Unicode

Total characters20377
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd
ValueCountFrequency (%)
vinylsd 1025
30.8%
metalsd 450
13.5%
hdboard 442
13.3%
wd 411
12.3%
sdng 411
12.3%
plywood 221
 
6.6%
cemntbd 126
 
3.8%
brkface 87
 
2.6%
wdshing 56
 
1.7%
asbshng 44
 
1.3%
Other values (6) 56
 
1.7%
2024-10-24T14:28:29.866352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 3584
17.6%
S 2034
 
10.0%
l 1698
 
8.3%
n 1666
 
8.2%
y 1246
 
6.1%
i 1081
 
5.3%
V 1025
 
5.0%
a 979
 
4.8%
o 937
 
4.6%
e 665
 
3.3%
Other values (22) 5462
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 3584
17.6%
S 2034
 
10.0%
l 1698
 
8.3%
n 1666
 
8.2%
y 1246
 
6.1%
i 1081
 
5.3%
V 1025
 
5.0%
a 979
 
4.8%
o 937
 
4.6%
e 665
 
3.3%
Other values (22) 5462
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 3584
17.6%
S 2034
 
10.0%
l 1698
 
8.3%
n 1666
 
8.2%
y 1246
 
6.1%
i 1081
 
5.3%
V 1025
 
5.0%
a 979
 
4.8%
o 937
 
4.6%
e 665
 
3.3%
Other values (22) 5462
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 3584
17.6%
S 2034
 
10.0%
l 1698
 
8.3%
n 1666
 
8.2%
y 1246
 
6.1%
i 1081
 
5.3%
V 1025
 
5.0%
a 979
 
4.8%
o 937
 
4.6%
e 665
 
3.3%
Other values (22) 5462
26.8%
Distinct16
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Memory size45.6 KiB
2024-10-24T14:28:30.020560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.978067169
Min length5

Characters and Unicode

Total characters20362
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd
ValueCountFrequency (%)
vinylsd 1014
29.7%
wd 472
13.8%
metalsd 447
13.1%
hdboard 406
11.9%
sdng 391
 
11.5%
plywood 270
 
7.9%
cmentbd 126
 
3.7%
shng 81
 
2.4%
brkface 47
 
1.4%
stucco 47
 
1.4%
Other values (8) 111
 
3.3%
2024-10-24T14:28:30.306595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 3532
17.3%
S 2043
 
10.0%
l 1734
 
8.5%
n 1682
 
8.3%
y 1284
 
6.3%
V 1014
 
5.0%
i 1014
 
5.0%
o 1002
 
4.9%
a 900
 
4.4%
t 642
 
3.2%
Other values (23) 5515
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 3532
17.3%
S 2043
 
10.0%
l 1734
 
8.5%
n 1682
 
8.3%
y 1284
 
6.3%
V 1014
 
5.0%
i 1014
 
5.0%
o 1002
 
4.9%
a 900
 
4.4%
t 642
 
3.2%
Other values (23) 5515
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 3532
17.3%
S 2043
 
10.0%
l 1734
 
8.5%
n 1682
 
8.3%
y 1284
 
6.3%
V 1014
 
5.0%
i 1014
 
5.0%
o 1002
 
4.9%
a 900
 
4.4%
t 642
 
3.2%
Other values (23) 5515
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 3532
17.3%
S 2043
 
10.0%
l 1734
 
8.5%
n 1682
 
8.3%
y 1284
 
6.3%
V 1014
 
5.0%
i 1014
 
5.0%
o 1002
 
4.9%
a 900
 
4.4%
t 642
 
3.2%
Other values (23) 5515
27.1%

MasVnrType
Text

MISSING 

Distinct3
Distinct (%)0.3%
Missing1766
Missing (%)60.5%
Memory size45.6 KiB
2024-10-24T14:28:30.434519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.546400694
Min length5

Characters and Unicode

Total characters7548
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrkFace
2nd rowBrkFace
3rd rowBrkFace
4th rowStone
5th rowStone
ValueCountFrequency (%)
brkface 879
76.2%
stone 249
 
21.6%
brkcmn 25
 
2.2%
2024-10-24T14:28:30.687303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1128
14.9%
B 904
12.0%
r 904
12.0%
k 904
12.0%
F 879
11.6%
a 879
11.6%
c 879
11.6%
n 274
 
3.6%
S 249
 
3.3%
t 249
 
3.3%
Other values (3) 299
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7548
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1128
14.9%
B 904
12.0%
r 904
12.0%
k 904
12.0%
F 879
11.6%
a 879
11.6%
c 879
11.6%
n 274
 
3.6%
S 249
 
3.3%
t 249
 
3.3%
Other values (3) 299
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7548
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1128
14.9%
B 904
12.0%
r 904
12.0%
k 904
12.0%
F 879
11.6%
a 879
11.6%
c 879
11.6%
n 274
 
3.6%
S 249
 
3.3%
t 249
 
3.3%
Other values (3) 299
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7548
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1128
14.9%
B 904
12.0%
r 904
12.0%
k 904
12.0%
F 879
11.6%
a 879
11.6%
c 879
11.6%
n 274
 
3.6%
S 249
 
3.3%
t 249
 
3.3%
Other values (3) 299
 
4.0%

MasVnrArea
Real number (ℝ)

ZEROS 

Distinct444
Distinct (%)15.3%
Missing23
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean102.2013122
Minimum0
Maximum1600
Zeros1738
Zeros (%)59.5%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:30.830240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3164
95-th percentile466.5
Maximum1600
Range1600
Interquartile range (IQR)164

Descriptive statistics

Standard deviation179.334253
Coefficient of variation (CV)1.754715759
Kurtosis9.254343333
Mean102.2013122
Median Absolute Deviation (MAD)0
Skewness2.602588512
Sum295975
Variance32160.77431
MonotonicityNot monotonic
2024-10-24T14:28:30.977605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1738
59.5%
120 15
 
0.5%
176 13
 
0.4%
200 13
 
0.4%
216 12
 
0.4%
180 12
 
0.4%
144 11
 
0.4%
72 11
 
0.4%
108 11
 
0.4%
16 11
 
0.4%
Other values (434) 1049
35.9%
(Missing) 23
 
0.8%
ValueCountFrequency (%)
0 1738
59.5%
1 3
 
0.1%
3 1
 
< 0.1%
11 1
 
< 0.1%
14 4
 
0.1%
ValueCountFrequency (%)
1600 1
< 0.1%
1378 1
< 0.1%
1290 1
< 0.1%
1224 2
0.1%
1170 1
< 0.1%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:31.095243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5838
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
ta 1798
61.6%
gd 979
33.5%
ex 107
 
3.7%
fa 35
 
1.2%
2024-10-24T14:28:31.319076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1798
30.8%
A 1798
30.8%
G 979
16.8%
d 979
16.8%
E 107
 
1.8%
x 107
 
1.8%
F 35
 
0.6%
a 35
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1798
30.8%
A 1798
30.8%
G 979
16.8%
d 979
16.8%
E 107
 
1.8%
x 107
 
1.8%
F 35
 
0.6%
a 35
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1798
30.8%
A 1798
30.8%
G 979
16.8%
d 979
16.8%
E 107
 
1.8%
x 107
 
1.8%
F 35
 
0.6%
a 35
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1798
30.8%
A 1798
30.8%
G 979
16.8%
d 979
16.8%
E 107
 
1.8%
x 107
 
1.8%
F 35
 
0.6%
a 35
 
0.6%
Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:31.423230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5838
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta 2538
86.9%
gd 299
 
10.2%
fa 67
 
2.3%
ex 12
 
0.4%
po 3
 
0.1%
2024-10-24T14:28:31.632358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 2538
43.5%
A 2538
43.5%
G 299
 
5.1%
d 299
 
5.1%
F 67
 
1.1%
a 67
 
1.1%
E 12
 
0.2%
x 12
 
0.2%
P 3
 
0.1%
o 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2538
43.5%
A 2538
43.5%
G 299
 
5.1%
d 299
 
5.1%
F 67
 
1.1%
a 67
 
1.1%
E 12
 
0.2%
x 12
 
0.2%
P 3
 
0.1%
o 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2538
43.5%
A 2538
43.5%
G 299
 
5.1%
d 299
 
5.1%
F 67
 
1.1%
a 67
 
1.1%
E 12
 
0.2%
x 12
 
0.2%
P 3
 
0.1%
o 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2538
43.5%
A 2538
43.5%
G 299
 
5.1%
d 299
 
5.1%
F 67
 
1.1%
a 67
 
1.1%
E 12
 
0.2%
x 12
 
0.2%
P 3
 
0.1%
o 3
 
0.1%
Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:31.759762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.51113395
Min length4

Characters and Unicode

Total characters16087
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc
ValueCountFrequency (%)
pconc 1308
44.8%
cblock 1235
42.3%
brktil 311
 
10.7%
slab 49
 
1.7%
stone 11
 
0.4%
wood 5
 
0.2%
2024-10-24T14:28:32.031638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 2564
15.9%
C 2543
15.8%
c 2543
15.8%
l 1595
9.9%
B 1546
9.6%
k 1546
9.6%
n 1319
8.2%
P 1308
8.1%
i 311
 
1.9%
T 311
 
1.9%
Other values (8) 501
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2564
15.9%
C 2543
15.8%
c 2543
15.8%
l 1595
9.9%
B 1546
9.6%
k 1546
9.6%
n 1319
8.2%
P 1308
8.1%
i 311
 
1.9%
T 311
 
1.9%
Other values (8) 501
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2564
15.9%
C 2543
15.8%
c 2543
15.8%
l 1595
9.9%
B 1546
9.6%
k 1546
9.6%
n 1319
8.2%
P 1308
8.1%
i 311
 
1.9%
T 311
 
1.9%
Other values (8) 501
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2564
15.9%
C 2543
15.8%
c 2543
15.8%
l 1595
9.9%
B 1546
9.6%
k 1546
9.6%
n 1319
8.2%
P 1308
8.1%
i 311
 
1.9%
T 311
 
1.9%
Other values (8) 501
 
3.1%

BsmtQual
Text

MISSING 

Distinct4
Distinct (%)0.1%
Missing81
Missing (%)2.8%
Memory size45.6 KiB
2024-10-24T14:28:32.157674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5676
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
ta 1283
45.2%
gd 1209
42.6%
ex 258
 
9.1%
fa 88
 
3.1%
2024-10-24T14:28:32.387343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1283
22.6%
A 1283
22.6%
G 1209
21.3%
d 1209
21.3%
E 258
 
4.5%
x 258
 
4.5%
F 88
 
1.6%
a 88
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1283
22.6%
A 1283
22.6%
G 1209
21.3%
d 1209
21.3%
E 258
 
4.5%
x 258
 
4.5%
F 88
 
1.6%
a 88
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1283
22.6%
A 1283
22.6%
G 1209
21.3%
d 1209
21.3%
E 258
 
4.5%
x 258
 
4.5%
F 88
 
1.6%
a 88
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1283
22.6%
A 1283
22.6%
G 1209
21.3%
d 1209
21.3%
E 258
 
4.5%
x 258
 
4.5%
F 88
 
1.6%
a 88
 
1.6%

BsmtCond
Text

MISSING 

Distinct4
Distinct (%)0.1%
Missing82
Missing (%)2.8%
Memory size45.6 KiB
2024-10-24T14:28:32.490294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5674
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA
ValueCountFrequency (%)
ta 2606
91.9%
gd 122
 
4.3%
fa 104
 
3.7%
po 5
 
0.2%
2024-10-24T14:28:32.701438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 2606
45.9%
A 2606
45.9%
G 122
 
2.2%
d 122
 
2.2%
F 104
 
1.8%
a 104
 
1.8%
P 5
 
0.1%
o 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2606
45.9%
A 2606
45.9%
G 122
 
2.2%
d 122
 
2.2%
F 104
 
1.8%
a 104
 
1.8%
P 5
 
0.1%
o 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2606
45.9%
A 2606
45.9%
G 122
 
2.2%
d 122
 
2.2%
F 104
 
1.8%
a 104
 
1.8%
P 5
 
0.1%
o 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2606
45.9%
A 2606
45.9%
G 122
 
2.2%
d 122
 
2.2%
F 104
 
1.8%
a 104
 
1.8%
P 5
 
0.1%
o 5
 
0.1%

BsmtExposure
Text

MISSING 

Distinct4
Distinct (%)0.1%
Missing82
Missing (%)2.8%
Memory size45.6 KiB
2024-10-24T14:28:32.790251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5674
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv
ValueCountFrequency (%)
no 1904
67.1%
av 418
 
14.7%
gd 276
 
9.7%
mn 239
 
8.4%
2024-10-24T14:28:32.992564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1904
33.6%
o 1904
33.6%
A 418
 
7.4%
v 418
 
7.4%
G 276
 
4.9%
d 276
 
4.9%
M 239
 
4.2%
n 239
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1904
33.6%
o 1904
33.6%
A 418
 
7.4%
v 418
 
7.4%
G 276
 
4.9%
d 276
 
4.9%
M 239
 
4.2%
n 239
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1904
33.6%
o 1904
33.6%
A 418
 
7.4%
v 418
 
7.4%
G 276
 
4.9%
d 276
 
4.9%
M 239
 
4.2%
n 239
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1904
33.6%
o 1904
33.6%
A 418
 
7.4%
v 418
 
7.4%
G 276
 
4.9%
d 276
 
4.9%
M 239
 
4.2%
n 239
 
4.2%

BsmtFinType1
Text

MISSING 

Distinct6
Distinct (%)0.2%
Missing79
Missing (%)2.7%
Memory size45.6 KiB
2024-10-24T14:28:33.126317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8520
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ
ValueCountFrequency (%)
unf 851
30.0%
glq 849
29.9%
alq 429
15.1%
rec 288
 
10.1%
blq 269
 
9.5%
lwq 154
 
5.4%
2024-10-24T14:28:33.369243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 1701
20.0%
Q 1701
20.0%
U 851
10.0%
n 851
10.0%
f 851
10.0%
G 849
10.0%
A 429
 
5.0%
R 288
 
3.4%
e 288
 
3.4%
c 288
 
3.4%
Other values (2) 423
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 1701
20.0%
Q 1701
20.0%
U 851
10.0%
n 851
10.0%
f 851
10.0%
G 849
10.0%
A 429
 
5.0%
R 288
 
3.4%
e 288
 
3.4%
c 288
 
3.4%
Other values (2) 423
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 1701
20.0%
Q 1701
20.0%
U 851
10.0%
n 851
10.0%
f 851
10.0%
G 849
10.0%
A 429
 
5.0%
R 288
 
3.4%
e 288
 
3.4%
c 288
 
3.4%
Other values (2) 423
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 1701
20.0%
Q 1701
20.0%
U 851
10.0%
n 851
10.0%
f 851
10.0%
G 849
10.0%
A 429
 
5.0%
R 288
 
3.4%
e 288
 
3.4%
c 288
 
3.4%
Other values (2) 423
 
5.0%

BsmtFinSF1
Real number (ℝ)

ZEROS 

Distinct991
Distinct (%)34.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean441.4232351
Minimum0
Maximum5644
Zeros929
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:33.508345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median368.5
Q3733
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)733

Descriptive statistics

Standard deviation455.6108259
Coefficient of variation (CV)1.032140562
Kurtosis6.904832097
Mean441.4232351
Median Absolute Deviation (MAD)368.5
Skewness1.425722288
Sum1288073
Variance207581.2247
MonotonicityNot monotonic
2024-10-24T14:28:33.638550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 929
31.8%
24 27
 
0.9%
16 14
 
0.5%
300 9
 
0.3%
20 8
 
0.3%
600 8
 
0.3%
384 8
 
0.3%
288 8
 
0.3%
500 7
 
0.2%
602 7
 
0.2%
Other values (981) 1893
64.9%
ValueCountFrequency (%)
0 929
31.8%
2 1
 
< 0.1%
16 14
 
0.5%
20 8
 
0.3%
24 27
 
0.9%
ValueCountFrequency (%)
5644 1
< 0.1%
4010 1
< 0.1%
2288 1
< 0.1%
2260 1
< 0.1%
2257 1
< 0.1%

BsmtFinType2
Text

MISSING 

Distinct6
Distinct (%)0.2%
Missing80
Missing (%)2.7%
Memory size45.6 KiB
2024-10-24T14:28:33.734650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8517
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf
ValueCountFrequency (%)
unf 2493
87.8%
rec 105
 
3.7%
lwq 87
 
3.1%
blq 68
 
2.4%
alq 52
 
1.8%
glq 34
 
1.2%
2024-10-24T14:28:33.949303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 2493
29.3%
n 2493
29.3%
f 2493
29.3%
L 241
 
2.8%
Q 241
 
2.8%
R 105
 
1.2%
e 105
 
1.2%
c 105
 
1.2%
w 87
 
1.0%
B 68
 
0.8%
Other values (2) 86
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8517
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 2493
29.3%
n 2493
29.3%
f 2493
29.3%
L 241
 
2.8%
Q 241
 
2.8%
R 105
 
1.2%
e 105
 
1.2%
c 105
 
1.2%
w 87
 
1.0%
B 68
 
0.8%
Other values (2) 86
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8517
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 2493
29.3%
n 2493
29.3%
f 2493
29.3%
L 241
 
2.8%
Q 241
 
2.8%
R 105
 
1.2%
e 105
 
1.2%
c 105
 
1.2%
w 87
 
1.0%
B 68
 
0.8%
Other values (2) 86
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8517
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 2493
29.3%
n 2493
29.3%
f 2493
29.3%
L 241
 
2.8%
Q 241
 
2.8%
R 105
 
1.2%
e 105
 
1.2%
c 105
 
1.2%
w 87
 
1.0%
B 68
 
0.8%
Other values (2) 86
 
1.0%

BsmtFinSF2
Real number (ℝ)

ZEROS 

Distinct272
Distinct (%)9.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean49.58224812
Minimum0
Maximum1526
Zeros2571
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:34.094379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile435
Maximum1526
Range1526
Interquartile range (IQR)0

Descriptive statistics

Standard deviation169.2056111
Coefficient of variation (CV)3.412624831
Kurtosis18.83653973
Mean49.58224812
Median Absolute Deviation (MAD)0
Skewness4.147455702
Sum144681
Variance28630.53883
MonotonicityNot monotonic
2024-10-24T14:28:34.237273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2571
88.1%
180 5
 
0.2%
294 5
 
0.2%
483 3
 
0.1%
168 3
 
0.1%
435 3
 
0.1%
539 3
 
0.1%
147 3
 
0.1%
144 3
 
0.1%
374 3
 
0.1%
Other values (262) 316
 
10.8%
ValueCountFrequency (%)
0 2571
88.1%
6 1
 
< 0.1%
12 1
 
< 0.1%
28 1
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
1526 1
< 0.1%
1474 1
< 0.1%
1393 1
< 0.1%
1164 1
< 0.1%
1127 1
< 0.1%

BsmtUnfSF
Real number (ℝ)

ZEROS 

Distinct1135
Distinct (%)38.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean560.7721042
Minimum0
Maximum2336
Zeros241
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:34.383504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1220
median467
Q3805.5
95-th percentile1474.9
Maximum2336
Range2336
Interquartile range (IQR)585.5

Descriptive statistics

Standard deviation439.5436594
Coefficient of variation (CV)0.7838186959
Kurtosis0.4036169063
Mean560.7721042
Median Absolute Deviation (MAD)280
Skewness0.9198236977
Sum1636333
Variance193198.6285
MonotonicityNot monotonic
2024-10-24T14:28:34.520169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 241
 
8.3%
384 19
 
0.7%
728 14
 
0.5%
672 13
 
0.4%
600 12
 
0.4%
572 11
 
0.4%
100 11
 
0.4%
216 11
 
0.4%
816 11
 
0.4%
270 10
 
0.3%
Other values (1125) 2565
87.9%
ValueCountFrequency (%)
0 241
8.3%
14 1
 
< 0.1%
15 1
 
< 0.1%
17 1
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
2336 1
< 0.1%
2153 1
< 0.1%
2140 1
< 0.1%
2121 1
< 0.1%
2062 1
< 0.1%

TotalBsmtSF
Real number (ℝ)

ZEROS 

Distinct1058
Distinct (%)36.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1051.777587
Minimum0
Maximum6110
Zeros78
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:34.646438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile455.25
Q1793
median989.5
Q31302
95-th percentile1776.15
Maximum6110
Range6110
Interquartile range (IQR)509

Descriptive statistics

Standard deviation440.7662581
Coefficient of variation (CV)0.4190679317
Kurtosis9.151099191
Mean1051.777587
Median Absolute Deviation (MAD)236.5
Skewness1.162882475
Sum3069087
Variance194274.8943
MonotonicityNot monotonic
2024-10-24T14:28:34.782835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
2.7%
864 74
 
2.5%
672 29
 
1.0%
912 26
 
0.9%
1040 25
 
0.9%
768 24
 
0.8%
816 23
 
0.8%
728 20
 
0.7%
780 19
 
0.7%
1008 19
 
0.7%
Other values (1048) 2581
88.4%
ValueCountFrequency (%)
0 78
2.7%
105 1
 
< 0.1%
160 1
 
< 0.1%
173 1
 
< 0.1%
190 1
 
< 0.1%
ValueCountFrequency (%)
6110 1
< 0.1%
5095 1
< 0.1%
3206 1
< 0.1%
3200 1
< 0.1%
3138 1
< 0.1%
Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:34.890582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.000342583
Min length4

Characters and Unicode

Total characters11677
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA
ValueCountFrequency (%)
gasa 2874
98.5%
gasw 27
 
0.9%
grav 9
 
0.3%
wall 6
 
0.2%
othw 2
 
0.1%
floor 1
 
< 0.1%
2024-10-24T14:28:35.127441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2916
25.0%
G 2910
24.9%
s 2901
24.8%
A 2874
24.6%
W 35
 
0.3%
l 13
 
0.1%
r 10
 
0.1%
v 9
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2916
25.0%
G 2910
24.9%
s 2901
24.8%
A 2874
24.6%
W 35
 
0.3%
l 13
 
0.1%
r 10
 
0.1%
v 9
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2916
25.0%
G 2910
24.9%
s 2901
24.8%
A 2874
24.6%
W 35
 
0.3%
l 13
 
0.1%
r 10
 
0.1%
v 9
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2916
25.0%
G 2910
24.9%
s 2901
24.8%
A 2874
24.6%
W 35
 
0.3%
l 13
 
0.1%
r 10
 
0.1%
v 9
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
< 0.1%
Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:35.229209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5838
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx
ValueCountFrequency (%)
ex 1493
51.1%
ta 857
29.4%
gd 474
 
16.2%
fa 92
 
3.2%
po 3
 
0.1%
2024-10-24T14:28:35.716622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1493
25.6%
x 1493
25.6%
T 857
14.7%
A 857
14.7%
G 474
 
8.1%
d 474
 
8.1%
F 92
 
1.6%
a 92
 
1.6%
P 3
 
0.1%
o 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1493
25.6%
x 1493
25.6%
T 857
14.7%
A 857
14.7%
G 474
 
8.1%
d 474
 
8.1%
F 92
 
1.6%
a 92
 
1.6%
P 3
 
0.1%
o 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1493
25.6%
x 1493
25.6%
T 857
14.7%
A 857
14.7%
G 474
 
8.1%
d 474
 
8.1%
F 92
 
1.6%
a 92
 
1.6%
P 3
 
0.1%
o 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1493
25.6%
x 1493
25.6%
T 857
14.7%
A 857
14.7%
G 474
 
8.1%
d 474
 
8.1%
F 92
 
1.6%
a 92
 
1.6%
P 3
 
0.1%
o 3
 
0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:35.802675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2919
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 2723
93.3%
n 196
 
6.7%
2024-10-24T14:28:36.000791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 2723
93.3%
N 196
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2919
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 2723
93.3%
N 196
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2919
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 2723
93.3%
N 196
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2919
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 2723
93.3%
N 196
 
6.7%
Distinct5
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size45.6 KiB
2024-10-24T14:28:36.122409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.999314599
Min length3

Characters and Unicode

Total characters14588
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr
ValueCountFrequency (%)
sbrkr 2671
91.5%
fusea 188
 
6.4%
fusef 50
 
1.7%
fusep 8
 
0.3%
mix 1
 
< 0.1%
2024-10-24T14:28:36.377696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 5342
36.6%
S 2671
18.3%
B 2671
18.3%
k 2671
18.3%
F 296
 
2.0%
u 246
 
1.7%
s 246
 
1.7%
e 246
 
1.7%
A 188
 
1.3%
P 8
 
0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 5342
36.6%
S 2671
18.3%
B 2671
18.3%
k 2671
18.3%
F 296
 
2.0%
u 246
 
1.7%
s 246
 
1.7%
e 246
 
1.7%
A 188
 
1.3%
P 8
 
0.1%
Other values (3) 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 5342
36.6%
S 2671
18.3%
B 2671
18.3%
k 2671
18.3%
F 296
 
2.0%
u 246
 
1.7%
s 246
 
1.7%
e 246
 
1.7%
A 188
 
1.3%
P 8
 
0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 5342
36.6%
S 2671
18.3%
B 2671
18.3%
k 2671
18.3%
F 296
 
2.0%
u 246
 
1.7%
s 246
 
1.7%
e 246
 
1.7%
A 188
 
1.3%
P 8
 
0.1%
Other values (3) 3
 
< 0.1%

1stFlrSF
Real number (ℝ)

Distinct1083
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.581706
Minimum334
Maximum5095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:36.518681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile665.9
Q1876
median1082
Q31387.5
95-th percentile1830.1
Maximum5095
Range4761
Interquartile range (IQR)511.5

Descriptive statistics

Standard deviation392.3620787
Coefficient of variation (CV)0.3383651851
Kurtosis6.956479038
Mean1159.581706
Median Absolute Deviation (MAD)235
Skewness1.470360106
Sum3384819
Variance153948.0008
MonotonicityNot monotonic
2024-10-24T14:28:36.660573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 46
 
1.6%
1040 28
 
1.0%
912 19
 
0.7%
848 18
 
0.6%
816 18
 
0.6%
960 18
 
0.6%
672 17
 
0.6%
894 17
 
0.6%
936 17
 
0.6%
546 15
 
0.5%
Other values (1073) 2706
92.7%
ValueCountFrequency (%)
334 1
< 0.1%
372 1
< 0.1%
407 1
< 0.1%
432 1
< 0.1%
438 1
< 0.1%
ValueCountFrequency (%)
5095 1
< 0.1%
4692 1
< 0.1%
3820 1
< 0.1%
3228 1
< 0.1%
3138 1
< 0.1%

2ndFlrSF
Real number (ℝ)

ZEROS 

Distinct635
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.4837273
Minimum0
Maximum2065
Zeros1668
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:36.797187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3704
95-th percentile1131.2
Maximum2065
Range2065
Interquartile range (IQR)704

Descriptive statistics

Standard deviation428.7014555
Coefficient of variation (CV)1.274062966
Kurtosis-0.4222606699
Mean336.4837273
Median Absolute Deviation (MAD)0
Skewness0.8621178326
Sum982196
Variance183784.938
MonotonicityNot monotonic
2024-10-24T14:28:36.939157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1668
57.1%
546 23
 
0.8%
728 18
 
0.6%
504 17
 
0.6%
720 13
 
0.4%
600 13
 
0.4%
672 13
 
0.4%
896 11
 
0.4%
886 10
 
0.3%
756 9
 
0.3%
Other values (625) 1124
38.5%
ValueCountFrequency (%)
0 1668
57.1%
110 1
 
< 0.1%
125 1
 
< 0.1%
144 1
 
< 0.1%
167 1
 
< 0.1%
ValueCountFrequency (%)
2065 1
< 0.1%
1872 1
< 0.1%
1862 1
< 0.1%
1836 1
< 0.1%
1818 1
< 0.1%

LowQualFinSF
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.694415896
Minimum0
Maximum1064
Zeros2879
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:37.070333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1064
Range1064
Interquartile range (IQR)0

Descriptive statistics

Standard deviation46.39682452
Coefficient of variation (CV)9.883407339
Kurtosis174.9328124
Mean4.694415896
Median Absolute Deviation (MAD)0
Skewness12.09497719
Sum13703
Variance2152.665325
MonotonicityNot monotonic
2024-10-24T14:28:37.207736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 2879
98.6%
80 4
 
0.1%
205 2
 
0.1%
360 2
 
0.1%
512 1
 
< 0.1%
697 1
 
< 0.1%
108 1
 
< 0.1%
312 1
 
< 0.1%
259 1
 
< 0.1%
514 1
 
< 0.1%
Other values (26) 26
 
0.9%
ValueCountFrequency (%)
0 2879
98.6%
53 1
 
< 0.1%
80 4
 
0.1%
108 1
 
< 0.1%
114 1
 
< 0.1%
ValueCountFrequency (%)
1064 1
< 0.1%
697 1
< 0.1%
572 1
< 0.1%
528 1
< 0.1%
515 1
< 0.1%

GrLivArea
Real number (ℝ)

Distinct1292
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1500.759849
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:37.348930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile861
Q11126
median1444
Q31743.5
95-th percentile2464.2
Maximum5642
Range5308
Interquartile range (IQR)617.5

Descriptive statistics

Standard deviation506.0510451
Coefficient of variation (CV)0.337196551
Kurtosis4.121603735
Mean1500.759849
Median Absolute Deviation (MAD)313
Skewness1.270010408
Sum4380718
Variance256087.6603
MonotonicityNot monotonic
2024-10-24T14:28:37.498734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 41
 
1.4%
1092 26
 
0.9%
1040 25
 
0.9%
1456 20
 
0.7%
1200 18
 
0.6%
894 15
 
0.5%
912 14
 
0.5%
816 14
 
0.5%
848 13
 
0.4%
1728 13
 
0.4%
Other values (1282) 2720
93.2%
ValueCountFrequency (%)
334 1
< 0.1%
407 1
< 0.1%
438 1
< 0.1%
480 1
< 0.1%
492 1
< 0.1%
ValueCountFrequency (%)
5642 1
< 0.1%
5095 1
< 0.1%
4676 1
< 0.1%
4476 1
< 0.1%
4316 1
< 0.1%

BsmtFullBath
Real number (ℝ)

ZEROS 

Distinct4
Distinct (%)0.1%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.4298937264
Minimum0
Maximum3
Zeros1705
Zeros (%)58.4%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:37.611152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5247356337
Coefficient of variation (CV)1.2206171
Kurtosis-0.7356934433
Mean0.4298937264
Median Absolute Deviation (MAD)0
Skewness0.6240621985
Sum1254
Variance0.2753474853
MonotonicityNot monotonic
2024-10-24T14:28:37.718316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0 1705
58.4%
1 1172
40.2%
2 38
 
1.3%
3 2
 
0.1%
(Missing) 2
 
0.1%
ValueCountFrequency (%)
0 1705
58.4%
1 1172
40.2%
2 38
 
1.3%
3 2
 
0.1%
ValueCountFrequency (%)
3 2
 
0.1%
2 38
 
1.3%
1 1172
40.2%
0 1705
58.4%

BsmtHalfBath
Real number (ℝ)

ZEROS 

Distinct3
Distinct (%)0.1%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.0613644155
Minimum0
Maximum2
Zeros2742
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:37.822209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2456869164
Coefficient of variation (CV)4.003735951
Kurtosis14.84807931
Mean0.0613644155
Median Absolute Deviation (MAD)0
Skewness3.932018208
Sum179
Variance0.06036206091
MonotonicityNot monotonic
2024-10-24T14:28:37.940284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 2742
93.9%
1 171
 
5.9%
2 4
 
0.1%
(Missing) 2
 
0.1%
ValueCountFrequency (%)
0 2742
93.9%
1 171
 
5.9%
2 4
 
0.1%
ValueCountFrequency (%)
2 4
 
0.1%
1 171
 
5.9%
0 2742
93.9%

FullBath
Real number (ℝ)

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.568002741
Minimum0
Maximum4
Zeros12
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:38.056732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5529692596
Coefficient of variation (CV)0.3526583502
Kurtosis-0.5381293607
Mean1.568002741
Median Absolute Deviation (MAD)0
Skewness0.1676919182
Sum4577
Variance0.305775002
MonotonicityNot monotonic
2024-10-24T14:28:38.164428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2 1530
52.4%
1 1309
44.8%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%
ValueCountFrequency (%)
0 12
 
0.4%
1 1309
44.8%
2 1530
52.4%
3 64
 
2.2%
4 4
 
0.1%
ValueCountFrequency (%)
4 4
 
0.1%
3 64
 
2.2%
2 1530
52.4%
1 1309
44.8%
0 12
 
0.4%

HalfBath
Real number (ℝ)

ZEROS 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3802672148
Minimum0
Maximum2
Zeros1834
Zeros (%)62.8%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:38.291160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5028716002
Coefficient of variation (CV)1.322416397
Kurtosis-1.03344648
Mean0.3802672148
Median Absolute Deviation (MAD)0
Skewness0.6949236493
Sum1110
Variance0.2528798463
MonotonicityNot monotonic
2024-10-24T14:28:38.410007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 1834
62.8%
1 1060
36.3%
2 25
 
0.9%
ValueCountFrequency (%)
0 1834
62.8%
1 1060
36.3%
2 25
 
0.9%
ValueCountFrequency (%)
2 25
 
0.9%
1 1060
36.3%
0 1834
62.8%

BedroomAbvGr
Real number (ℝ)

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.860226105
Minimum0
Maximum8
Zeros8
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:38.531502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8226931007
Coefficient of variation (CV)0.2876321908
Kurtosis1.941403748
Mean2.860226105
Median Absolute Deviation (MAD)0
Skewness0.3264921493
Sum8349
Variance0.6768239379
MonotonicityNot monotonic
2024-10-24T14:28:38.654434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 1596
54.7%
2 742
25.4%
4 400
 
13.7%
1 103
 
3.5%
5 48
 
1.6%
6 21
 
0.7%
0 8
 
0.3%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 8
 
0.3%
1 103
 
3.5%
2 742
25.4%
3 1596
54.7%
4 400
 
13.7%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 21
 
0.7%
5 48
 
1.6%
4 400
 
13.7%
3 1596
54.7%

KitchenAbvGr
Real number (ℝ)

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0445358
Minimum0
Maximum3
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:38.761348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2144620012
Coefficient of variation (CV)0.2053179999
Kurtosis19.77793659
Mean1.0445358
Median Absolute Deviation (MAD)0
Skewness4.304466642
Sum3049
Variance0.04599394997
MonotonicityNot monotonic
2024-10-24T14:28:38.868284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
1 2785
95.4%
2 129
 
4.4%
0 3
 
0.1%
3 2
 
0.1%
ValueCountFrequency (%)
0 3
 
0.1%
1 2785
95.4%
2 129
 
4.4%
3 2
 
0.1%
ValueCountFrequency (%)
3 2
 
0.1%
2 129
 
4.4%
1 2785
95.4%
0 3
 
0.1%
Distinct4
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size45.6 KiB
2024-10-24T14:28:38.990442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5836
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd
ValueCountFrequency (%)
ta 1492
51.1%
gd 1151
39.4%
ex 205
 
7.0%
fa 70
 
2.4%
2024-10-24T14:28:39.237732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1492
25.6%
A 1492
25.6%
G 1151
19.7%
d 1151
19.7%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1492
25.6%
A 1492
25.6%
G 1151
19.7%
d 1151
19.7%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1492
25.6%
A 1492
25.6%
G 1151
19.7%
d 1151
19.7%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1492
25.6%
A 1492
25.6%
G 1151
19.7%
d 1151
19.7%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%

TotRmsAbvGrd
Real number (ℝ)

Distinct14
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.451524495
Minimum2
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:39.362512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile9
Maximum15
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.569379144
Coefficient of variation (CV)0.2432571007
Kurtosis1.169063585
Mean6.451524495
Median Absolute Deviation (MAD)1
Skewness0.7587568677
Sum18832
Variance2.462950897
MonotonicityNot monotonic
2024-10-24T14:28:39.475544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
6 844
28.9%
7 649
22.2%
5 583
20.0%
8 347
11.9%
4 196
 
6.7%
9 143
 
4.9%
10 80
 
2.7%
11 32
 
1.1%
3 25
 
0.9%
12 16
 
0.5%
Other values (4) 4
 
0.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 25
 
0.9%
4 196
 
6.7%
5 583
20.0%
6 844
28.9%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 16
0.5%
11 32
1.1%
Distinct7
Distinct (%)0.2%
Missing2
Missing (%)0.1%
Memory size45.6 KiB
2024-10-24T14:28:39.571473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.055879328
Min length3

Characters and Unicode

Total characters8914
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp
ValueCountFrequency (%)
typ 2717
93.1%
min2 70
 
2.4%
min1 65
 
2.2%
mod 35
 
1.2%
maj1 19
 
0.7%
maj2 9
 
0.3%
sev 2
 
0.1%
2024-10-24T14:28:39.796893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 2717
30.5%
y 2717
30.5%
p 2717
30.5%
M 198
 
2.2%
i 135
 
1.5%
n 135
 
1.5%
1 84
 
0.9%
2 79
 
0.9%
o 35
 
0.4%
d 35
 
0.4%
Other values (5) 62
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2717
30.5%
y 2717
30.5%
p 2717
30.5%
M 198
 
2.2%
i 135
 
1.5%
n 135
 
1.5%
1 84
 
0.9%
2 79
 
0.9%
o 35
 
0.4%
d 35
 
0.4%
Other values (5) 62
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2717
30.5%
y 2717
30.5%
p 2717
30.5%
M 198
 
2.2%
i 135
 
1.5%
n 135
 
1.5%
1 84
 
0.9%
2 79
 
0.9%
o 35
 
0.4%
d 35
 
0.4%
Other values (5) 62
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2717
30.5%
y 2717
30.5%
p 2717
30.5%
M 198
 
2.2%
i 135
 
1.5%
n 135
 
1.5%
1 84
 
0.9%
2 79
 
0.9%
o 35
 
0.4%
d 35
 
0.4%
Other values (5) 62
 
0.7%

Fireplaces
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5971223022
Minimum0
Maximum4
Zeros1420
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:39.916287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.646129359
Coefficient of variation (CV)1.082072059
Kurtosis0.07642384076
Mean0.5971223022
Median Absolute Deviation (MAD)1
Skewness0.7338717709
Sum1743
Variance0.4174831485
MonotonicityNot monotonic
2024-10-24T14:28:40.036271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 1420
48.6%
1 1268
43.4%
2 219
 
7.5%
3 11
 
0.4%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 1420
48.6%
1 1268
43.4%
2 219
 
7.5%
3 11
 
0.4%
4 1
 
< 0.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
3 11
 
0.4%
2 219
 
7.5%
1 1268
43.4%
0 1420
48.6%

FireplaceQu
Text

MISSING 

Distinct5
Distinct (%)0.3%
Missing1420
Missing (%)48.6%
Memory size45.6 KiB
2024-10-24T14:28:40.161376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2998
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
gd 744
49.6%
ta 592
39.5%
fa 74
 
4.9%
po 46
 
3.1%
ex 43
 
2.9%
2024-10-24T14:28:40.414589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 744
24.8%
d 744
24.8%
T 592
19.7%
A 592
19.7%
F 74
 
2.5%
a 74
 
2.5%
P 46
 
1.5%
o 46
 
1.5%
E 43
 
1.4%
x 43
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 744
24.8%
d 744
24.8%
T 592
19.7%
A 592
19.7%
F 74
 
2.5%
a 74
 
2.5%
P 46
 
1.5%
o 46
 
1.5%
E 43
 
1.4%
x 43
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 744
24.8%
d 744
24.8%
T 592
19.7%
A 592
19.7%
F 74
 
2.5%
a 74
 
2.5%
P 46
 
1.5%
o 46
 
1.5%
E 43
 
1.4%
x 43
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 744
24.8%
d 744
24.8%
T 592
19.7%
A 592
19.7%
F 74
 
2.5%
a 74
 
2.5%
P 46
 
1.5%
o 46
 
1.5%
E 43
 
1.4%
x 43
 
1.4%

GarageType
Text

MISSING 

Distinct6
Distinct (%)0.2%
Missing157
Missing (%)5.4%
Memory size45.6 KiB
2024-10-24T14:28:40.552247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.085807386
Min length6

Characters and Unicode

Total characters16809
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd
ValueCountFrequency (%)
attchd 1723
62.4%
detchd 779
28.2%
builtin 186
 
6.7%
basment 36
 
1.3%
2types 23
 
0.8%
carport 15
 
0.5%
2024-10-24T14:28:40.812877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 4462
26.5%
c 2502
14.9%
h 2502
14.9%
d 2502
14.9%
A 1723
 
10.3%
e 838
 
5.0%
D 779
 
4.6%
n 222
 
1.3%
B 222
 
1.3%
u 186
 
1.1%
Other values (14) 871
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16809
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 4462
26.5%
c 2502
14.9%
h 2502
14.9%
d 2502
14.9%
A 1723
 
10.3%
e 838
 
5.0%
D 779
 
4.6%
n 222
 
1.3%
B 222
 
1.3%
u 186
 
1.1%
Other values (14) 871
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16809
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 4462
26.5%
c 2502
14.9%
h 2502
14.9%
d 2502
14.9%
A 1723
 
10.3%
e 838
 
5.0%
D 779
 
4.6%
n 222
 
1.3%
B 222
 
1.3%
u 186
 
1.1%
Other values (14) 871
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16809
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 4462
26.5%
c 2502
14.9%
h 2502
14.9%
d 2502
14.9%
A 1723
 
10.3%
e 838
 
5.0%
D 779
 
4.6%
n 222
 
1.3%
B 222
 
1.3%
u 186
 
1.1%
Other values (14) 871
 
5.2%

GarageYrBlt
Real number (ℝ)

MISSING 

Distinct103
Distinct (%)3.7%
Missing159
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean1978.113406
Minimum1895
Maximum2207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:40.970555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1895
5-th percentile1928
Q11960
median1979
Q32002
95-th percentile2007
Maximum2207
Range312
Interquartile range (IQR)42

Descriptive statistics

Standard deviation25.57428472
Coefficient of variation (CV)0.01292862414
Kurtosis1.809844718
Mean1978.113406
Median Absolute Deviation (MAD)21
Skewness-0.382150161
Sum5459593
Variance654.0440391
MonotonicityNot monotonic
2024-10-24T14:28:41.108403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 142
 
4.9%
2007 115
 
3.9%
2006 115
 
3.9%
2004 99
 
3.4%
2003 92
 
3.2%
1977 66
 
2.3%
2008 61
 
2.1%
1998 58
 
2.0%
2000 55
 
1.9%
1999 54
 
1.8%
Other values (93) 1903
65.2%
(Missing) 159
 
5.4%
ValueCountFrequency (%)
1895 1
 
< 0.1%
1896 1
 
< 0.1%
1900 6
0.2%
1906 1
 
< 0.1%
1908 1
 
< 0.1%
ValueCountFrequency (%)
2207 1
 
< 0.1%
2010 5
 
0.2%
2009 29
 
1.0%
2008 61
2.1%
2007 115
3.9%

GarageFinish
Text

MISSING 

Distinct3
Distinct (%)0.1%
Missing159
Missing (%)5.4%
Memory size45.6 KiB
2024-10-24T14:28:41.225570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8280
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn
ValueCountFrequency (%)
unf 1230
44.6%
rfn 811
29.4%
fin 719
26.1%
2024-10-24T14:28:41.442186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2760
33.3%
F 1530
18.5%
U 1230
14.9%
f 1230
14.9%
R 811
 
9.8%
i 719
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2760
33.3%
F 1530
18.5%
U 1230
14.9%
f 1230
14.9%
R 811
 
9.8%
i 719
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2760
33.3%
F 1530
18.5%
U 1230
14.9%
f 1230
14.9%
R 811
 
9.8%
i 719
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2760
33.3%
F 1530
18.5%
U 1230
14.9%
f 1230
14.9%
R 811
 
9.8%
i 719
 
8.7%

GarageCars
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.766620973
Minimum0
Maximum5
Zeros157
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:41.558693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7616243226
Coefficient of variation (CV)0.4311192577
Kurtosis0.2381978193
Mean1.766620973
Median Absolute Deviation (MAD)0
Skewness-0.2183727666
Sum5155
Variance0.5800716088
MonotonicityNot monotonic
2024-10-24T14:28:41.662208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 1594
54.6%
1 776
26.6%
3 374
 
12.8%
0 157
 
5.4%
4 16
 
0.5%
5 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 157
 
5.4%
1 776
26.6%
2 1594
54.6%
3 374
 
12.8%
4 16
 
0.5%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 16
 
0.5%
3 374
 
12.8%
2 1594
54.6%
1 776
26.6%

GarageArea
Real number (ℝ)

ZEROS 

Distinct603
Distinct (%)20.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean472.8745716
Minimum0
Maximum1488
Zeros157
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:41.784953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1320
median480
Q3576
95-th percentile856.15
Maximum1488
Range1488
Interquartile range (IQR)256

Descriptive statistics

Standard deviation215.394815
Coefficient of variation (CV)0.4555009466
Kurtosis0.9397829054
Mean472.8745716
Median Absolute Deviation (MAD)124
Skewness0.2413005173
Sum1379848
Variance46394.92633
MonotonicityNot monotonic
2024-10-24T14:28:41.928784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157
 
5.4%
576 97
 
3.3%
440 96
 
3.3%
240 69
 
2.4%
484 68
 
2.3%
528 65
 
2.2%
400 58
 
2.0%
480 54
 
1.8%
264 51
 
1.7%
288 50
 
1.7%
Other values (593) 2153
73.8%
ValueCountFrequency (%)
0 157
5.4%
100 1
 
< 0.1%
160 3
 
0.1%
162 2
 
0.1%
164 2
 
0.1%
ValueCountFrequency (%)
1488 1
< 0.1%
1418 1
< 0.1%
1390 1
< 0.1%
1356 1
< 0.1%
1348 1
< 0.1%

GarageQual
Text

MISSING 

Distinct5
Distinct (%)0.2%
Missing159
Missing (%)5.4%
Memory size45.6 KiB
2024-10-24T14:28:42.030315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5520
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta 2604
94.3%
fa 124
 
4.5%
gd 24
 
0.9%
po 5
 
0.2%
ex 3
 
0.1%
2024-10-24T14:28:42.240324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 2604
47.2%
A 2604
47.2%
F 124
 
2.2%
a 124
 
2.2%
G 24
 
0.4%
d 24
 
0.4%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2604
47.2%
A 2604
47.2%
F 124
 
2.2%
a 124
 
2.2%
G 24
 
0.4%
d 24
 
0.4%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2604
47.2%
A 2604
47.2%
F 124
 
2.2%
a 124
 
2.2%
G 24
 
0.4%
d 24
 
0.4%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2604
47.2%
A 2604
47.2%
F 124
 
2.2%
a 124
 
2.2%
G 24
 
0.4%
d 24
 
0.4%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

GarageCond
Text

MISSING 

Distinct5
Distinct (%)0.2%
Missing159
Missing (%)5.4%
Memory size45.6 KiB
2024-10-24T14:28:42.354389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5520
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta 2654
96.2%
fa 74
 
2.7%
gd 15
 
0.5%
po 14
 
0.5%
ex 3
 
0.1%
2024-10-24T14:28:42.662227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 2654
48.1%
A 2654
48.1%
F 74
 
1.3%
a 74
 
1.3%
G 15
 
0.3%
d 15
 
0.3%
P 14
 
0.3%
o 14
 
0.3%
E 3
 
0.1%
x 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2654
48.1%
A 2654
48.1%
F 74
 
1.3%
a 74
 
1.3%
G 15
 
0.3%
d 15
 
0.3%
P 14
 
0.3%
o 14
 
0.3%
E 3
 
0.1%
x 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2654
48.1%
A 2654
48.1%
F 74
 
1.3%
a 74
 
1.3%
G 15
 
0.3%
d 15
 
0.3%
P 14
 
0.3%
o 14
 
0.3%
E 3
 
0.1%
x 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2654
48.1%
A 2654
48.1%
F 74
 
1.3%
a 74
 
1.3%
G 15
 
0.3%
d 15
 
0.3%
P 14
 
0.3%
o 14
 
0.3%
E 3
 
0.1%
x 3
 
0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:42.744373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2919
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 2641
90.5%
n 216
 
7.4%
p 62
 
2.1%
2024-10-24T14:28:42.942982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 2641
90.5%
N 216
 
7.4%
P 62
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2919
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 2641
90.5%
N 216
 
7.4%
P 62
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2919
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 2641
90.5%
N 216
 
7.4%
P 62
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2919
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 2641
90.5%
N 216
 
7.4%
P 62
 
2.1%

WoodDeckSF
Real number (ℝ)

ZEROS 

Distinct379
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.70983213
Minimum0
Maximum1424
Zeros1523
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:43.082721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile328
Maximum1424
Range1424
Interquartile range (IQR)168

Descriptive statistics

Standard deviation126.5265893
Coefficient of variation (CV)1.350195454
Kurtosis6.74155019
Mean93.70983213
Median Absolute Deviation (MAD)0
Skewness1.843380213
Sum273539
Variance16008.9778
MonotonicityNot monotonic
2024-10-24T14:28:43.253166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1523
52.2%
100 74
 
2.5%
192 70
 
2.4%
144 61
 
2.1%
168 56
 
1.9%
120 53
 
1.8%
140 29
 
1.0%
240 20
 
0.7%
224 19
 
0.7%
160 17
 
0.6%
Other values (369) 997
34.2%
ValueCountFrequency (%)
0 1523
52.2%
4 1
 
< 0.1%
12 2
 
0.1%
14 1
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
1424 1
< 0.1%
870 1
< 0.1%
857 1
< 0.1%
736 1
< 0.1%
728 1
< 0.1%

OpenPorchSF
Real number (ℝ)

ZEROS 

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.48681055
Minimum0
Maximum742
Zeros1298
Zeros (%)44.5%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:43.737677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26
Q370
95-th percentile183.1
Maximum742
Range742
Interquartile range (IQR)70

Descriptive statistics

Standard deviation67.57549339
Coefficient of variation (CV)1.423037105
Kurtosis10.93735252
Mean47.48681055
Median Absolute Deviation (MAD)26
Skewness2.536417316
Sum138614
Variance4566.447307
MonotonicityNot monotonic
2024-10-24T14:28:43.877530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1298
44.5%
36 52
 
1.8%
48 51
 
1.7%
40 44
 
1.5%
32 38
 
1.3%
24 36
 
1.2%
28 35
 
1.2%
20 33
 
1.1%
30 31
 
1.1%
50 29
 
1.0%
Other values (242) 1272
43.6%
ValueCountFrequency (%)
0 1298
44.5%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
10 2
 
0.1%
ValueCountFrequency (%)
742 1
< 0.1%
570 1
< 0.1%
547 1
< 0.1%
523 1
< 0.1%
502 1
< 0.1%

EnclosedPorch
Real number (ℝ)

ZEROS 

Distinct183
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.09832134
Minimum0
Maximum1012
Zeros2460
Zeros (%)84.3%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:44.029337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile176
Maximum1012
Range1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64.24424559
Coefficient of variation (CV)2.78133829
Kurtosis28.37790892
Mean23.09832134
Median Absolute Deviation (MAD)0
Skewness4.005950071
Sum67424
Variance4127.323092
MonotonicityNot monotonic
2024-10-24T14:28:44.167603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2460
84.3%
112 22
 
0.8%
96 13
 
0.4%
144 11
 
0.4%
192 10
 
0.3%
168 9
 
0.3%
120 9
 
0.3%
84 8
 
0.3%
116 8
 
0.3%
40 8
 
0.3%
Other values (173) 361
 
12.4%
ValueCountFrequency (%)
0 2460
84.3%
16 1
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
20 2
 
0.1%
ValueCountFrequency (%)
1012 1
< 0.1%
584 1
< 0.1%
552 1
< 0.1%
432 1
< 0.1%
429 1
< 0.1%

3SsnPorch
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.602261048
Minimum0
Maximum508
Zeros2882
Zeros (%)98.7%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:44.294484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.18816933
Coefficient of variation (CV)9.679339952
Kurtosis149.4098343
Mean2.602261048
Median Absolute Deviation (MAD)0
Skewness11.38191439
Sum7596
Variance634.4438743
MonotonicityNot monotonic
2024-10-24T14:28:44.436273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 2882
98.7%
168 3
 
0.1%
153 3
 
0.1%
144 2
 
0.1%
216 2
 
0.1%
180 2
 
0.1%
140 1
 
< 0.1%
86 1
 
< 0.1%
176 1
 
< 0.1%
219 1
 
< 0.1%
Other values (21) 21
 
0.7%
ValueCountFrequency (%)
0 2882
98.7%
23 1
 
< 0.1%
86 1
 
< 0.1%
96 1
 
< 0.1%
120 1
 
< 0.1%
ValueCountFrequency (%)
508 1
< 0.1%
407 1
< 0.1%
360 1
< 0.1%
323 1
< 0.1%
320 1
< 0.1%

ScreenPorch
Real number (ℝ)

ZEROS 

Distinct121
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.06235012
Minimum0
Maximum576
Zeros2663
Zeros (%)91.2%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:44.564680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile161
Maximum576
Range576
Interquartile range (IQR)0

Descriptive statistics

Standard deviation56.18436511
Coefficient of variation (CV)3.497891946
Kurtosis17.77670361
Mean16.06235012
Median Absolute Deviation (MAD)0
Skewness3.948723141
Sum46886
Variance3156.682883
MonotonicityNot monotonic
2024-10-24T14:28:44.701459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2663
91.2%
144 13
 
0.4%
192 11
 
0.4%
168 10
 
0.3%
120 9
 
0.3%
216 8
 
0.3%
200 7
 
0.2%
180 7
 
0.2%
160 6
 
0.2%
224 6
 
0.2%
Other values (111) 179
 
6.1%
ValueCountFrequency (%)
0 2663
91.2%
40 1
 
< 0.1%
53 1
 
< 0.1%
60 1
 
< 0.1%
63 1
 
< 0.1%
ValueCountFrequency (%)
576 1
< 0.1%
490 1
< 0.1%
480 1
< 0.1%
440 1
< 0.1%
410 1
< 0.1%

PoolArea
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.251798561
Minimum0
Maximum800
Zeros2906
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:44.829068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum800
Range800
Interquartile range (IQR)0

Descriptive statistics

Standard deviation35.66394597
Coefficient of variation (CV)15.83798239
Kurtosis298.6331436
Mean2.251798561
Median Absolute Deviation (MAD)0
Skewness16.90701724
Sum6573
Variance1271.917042
MonotonicityNot monotonic
2024-10-24T14:28:44.944608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 2906
99.6%
512 1
 
< 0.1%
648 1
 
< 0.1%
576 1
 
< 0.1%
555 1
 
< 0.1%
480 1
 
< 0.1%
519 1
 
< 0.1%
738 1
 
< 0.1%
144 1
 
< 0.1%
368 1
 
< 0.1%
Other values (4) 4
 
0.1%
ValueCountFrequency (%)
0 2906
99.6%
144 1
 
< 0.1%
228 1
 
< 0.1%
368 1
 
< 0.1%
444 1
 
< 0.1%
ValueCountFrequency (%)
800 1
< 0.1%
738 1
< 0.1%
648 1
< 0.1%
576 1
< 0.1%
561 1
< 0.1%

PoolQC
Text

MISSING 

Distinct3
Distinct (%)30.0%
Missing2909
Missing (%)99.7%
Memory size45.6 KiB
2024-10-24T14:28:45.061647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters20
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEx
2nd rowFa
3rd rowGd
4th rowEx
5th rowGd
ValueCountFrequency (%)
ex 4
40.0%
gd 4
40.0%
fa 2
20.0%
2024-10-24T14:28:45.286419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 4
20.0%
x 4
20.0%
G 4
20.0%
d 4
20.0%
F 2
10.0%
a 2
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 4
20.0%
x 4
20.0%
G 4
20.0%
d 4
20.0%
F 2
10.0%
a 2
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 4
20.0%
x 4
20.0%
G 4
20.0%
d 4
20.0%
F 2
10.0%
a 2
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 4
20.0%
x 4
20.0%
G 4
20.0%
d 4
20.0%
F 2
10.0%
a 2
10.0%

Fence
Text

MISSING 

Distinct4
Distinct (%)0.7%
Missing2348
Missing (%)80.4%
Memory size45.6 KiB
2024-10-24T14:28:45.401278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.782837128
Min length4

Characters and Unicode

Total characters2731
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMnPrv
2nd rowGdWo
3rd rowGdPrv
4th rowMnPrv
5th rowGdPrv
ValueCountFrequency (%)
mnprv 329
57.6%
gdprv 118
 
20.7%
gdwo 112
 
19.6%
mnww 12
 
2.1%
2024-10-24T14:28:45.748363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 447
16.4%
r 447
16.4%
v 447
16.4%
M 341
12.5%
n 341
12.5%
G 230
8.4%
d 230
8.4%
W 124
 
4.5%
o 112
 
4.1%
w 12
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 447
16.4%
r 447
16.4%
v 447
16.4%
M 341
12.5%
n 341
12.5%
G 230
8.4%
d 230
8.4%
W 124
 
4.5%
o 112
 
4.1%
w 12
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 447
16.4%
r 447
16.4%
v 447
16.4%
M 341
12.5%
n 341
12.5%
G 230
8.4%
d 230
8.4%
W 124
 
4.5%
o 112
 
4.1%
w 12
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 447
16.4%
r 447
16.4%
v 447
16.4%
M 341
12.5%
n 341
12.5%
G 230
8.4%
d 230
8.4%
W 124
 
4.5%
o 112
 
4.1%
w 12
 
0.4%

MiscFeature
Text

MISSING 

Distinct4
Distinct (%)3.8%
Missing2814
Missing (%)96.4%
Memory size45.6 KiB
2024-10-24T14:28:45.928593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters420
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowShed
2nd rowShed
3rd rowShed
4th rowShed
5th rowShed
ValueCountFrequency (%)
shed 95
90.5%
gar2 5
 
4.8%
othr 4
 
3.8%
tenc 1
 
1.0%
2024-10-24T14:28:46.262243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h 99
23.6%
e 96
22.9%
S 95
22.6%
d 95
22.6%
r 9
 
2.1%
G 5
 
1.2%
a 5
 
1.2%
2 5
 
1.2%
O 4
 
1.0%
t 4
 
1.0%
Other values (3) 3
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 99
23.6%
e 96
22.9%
S 95
22.6%
d 95
22.6%
r 9
 
2.1%
G 5
 
1.2%
a 5
 
1.2%
2 5
 
1.2%
O 4
 
1.0%
t 4
 
1.0%
Other values (3) 3
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 99
23.6%
e 96
22.9%
S 95
22.6%
d 95
22.6%
r 9
 
2.1%
G 5
 
1.2%
a 5
 
1.2%
2 5
 
1.2%
O 4
 
1.0%
t 4
 
1.0%
Other values (3) 3
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 99
23.6%
e 96
22.9%
S 95
22.6%
d 95
22.6%
r 9
 
2.1%
G 5
 
1.2%
a 5
 
1.2%
2 5
 
1.2%
O 4
 
1.0%
t 4
 
1.0%
Other values (3) 3
 
0.7%

MiscVal
Real number (ℝ)

SKEWED  ZEROS 

Distinct38
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.8259678
Minimum0
Maximum17000
Zeros2816
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:46.431287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum17000
Range17000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation567.4022106
Coefficient of variation (CV)11.16362826
Kurtosis564.0745818
Mean50.8259678
Median Absolute Deviation (MAD)0
Skewness21.95848032
Sum148361
Variance321945.2685
MonotonicityNot monotonic
2024-10-24T14:28:46.579285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 2816
96.5%
400 18
 
0.6%
500 13
 
0.4%
450 9
 
0.3%
600 8
 
0.3%
2000 7
 
0.2%
700 7
 
0.2%
1500 3
 
0.1%
650 3
 
0.1%
1200 3
 
0.1%
Other values (28) 32
 
1.1%
ValueCountFrequency (%)
0 2816
96.5%
54 1
 
< 0.1%
80 1
 
< 0.1%
300 1
 
< 0.1%
350 1
 
< 0.1%
ValueCountFrequency (%)
17000 1
< 0.1%
15500 1
< 0.1%
12500 1
< 0.1%
8300 1
< 0.1%
6500 1
< 0.1%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.213086674
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:46.723489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.714761774
Coefficient of variation (CV)0.4369425242
Kurtosis-0.454336588
Mean6.213086674
Median Absolute Deviation (MAD)2
Skewness0.195984669
Sum18136
Variance7.36993149
MonotonicityNot monotonic
2024-10-24T14:28:46.851617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 503
17.2%
7 446
15.3%
5 394
13.5%
4 279
9.6%
8 233
8.0%
3 232
7.9%
10 173
 
5.9%
9 158
 
5.4%
11 142
 
4.9%
2 133
 
4.6%
Other values (2) 226
7.7%
ValueCountFrequency (%)
1 122
 
4.2%
2 133
 
4.6%
3 232
7.9%
4 279
9.6%
5 394
13.5%
ValueCountFrequency (%)
12 104
3.6%
11 142
4.9%
10 173
5.9%
9 158
5.4%
8 233
8.0%

YrSold
Real number (ℝ)

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.792737
Minimum2006
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:46.966402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12007
median2008
Q32009
95-th percentile2010
Maximum2010
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.314964489
Coefficient of variation (CV)0.0006549303942
Kurtosis-1.155146558
Mean2007.792737
Median Absolute Deviation (MAD)1
Skewness0.1324668957
Sum5860747
Variance1.729131607
MonotonicityNot monotonic
2024-10-24T14:28:47.097390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2007 692
23.7%
2009 647
22.2%
2008 622
21.3%
2006 619
21.2%
2010 339
11.6%
ValueCountFrequency (%)
2006 619
21.2%
2007 692
23.7%
2008 622
21.3%
2009 647
22.2%
2010 339
11.6%
ValueCountFrequency (%)
2010 339
11.6%
2009 647
22.2%
2008 622
21.3%
2007 692
23.7%
2006 619
21.2%
Distinct9
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Memory size45.6 KiB
2024-10-24T14:28:47.206233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.16415353
Min length2

Characters and Unicode

Total characters6315
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD
ValueCountFrequency (%)
wd 2525
86.5%
new 239
 
8.2%
cod 87
 
3.0%
conld 26
 
0.9%
cwd 12
 
0.4%
conli 9
 
0.3%
conlw 8
 
0.3%
oth 7
 
0.2%
con 5
 
0.2%
2024-10-24T14:28:47.449524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 2650
42.0%
W 2537
40.2%
w 247
 
3.9%
N 239
 
3.8%
e 239
 
3.8%
C 147
 
2.3%
O 94
 
1.5%
o 48
 
0.8%
n 48
 
0.8%
L 43
 
0.7%
Other values (3) 23
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 2650
42.0%
W 2537
40.2%
w 247
 
3.9%
N 239
 
3.8%
e 239
 
3.8%
C 147
 
2.3%
O 94
 
1.5%
o 48
 
0.8%
n 48
 
0.8%
L 43
 
0.7%
Other values (3) 23
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 2650
42.0%
W 2537
40.2%
w 247
 
3.9%
N 239
 
3.8%
e 239
 
3.8%
C 147
 
2.3%
O 94
 
1.5%
o 48
 
0.8%
n 48
 
0.8%
L 43
 
0.7%
Other values (3) 23
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 2650
42.0%
W 2537
40.2%
w 247
 
3.9%
N 239
 
3.8%
e 239
 
3.8%
C 147
 
2.3%
O 94
 
1.5%
o 48
 
0.8%
n 48
 
0.8%
L 43
 
0.7%
Other values (3) 23
 
0.4%
Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:47.589170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.153134635
Min length6

Characters and Unicode

Total characters17961
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal
ValueCountFrequency (%)
normal 2402
82.3%
partial 245
 
8.4%
abnorml 190
 
6.5%
family 46
 
1.6%
alloca 24
 
0.8%
adjland 12
 
0.4%
2024-10-24T14:28:47.842784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2974
16.6%
l 2931
16.3%
r 2837
15.8%
m 2638
14.7%
o 2616
14.6%
N 2402
13.4%
i 291
 
1.6%
P 245
 
1.4%
t 245
 
1.4%
A 226
 
1.3%
Other values (8) 556
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2974
16.6%
l 2931
16.3%
r 2837
15.8%
m 2638
14.7%
o 2616
14.6%
N 2402
13.4%
i 291
 
1.6%
P 245
 
1.4%
t 245
 
1.4%
A 226
 
1.3%
Other values (8) 556
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2974
16.6%
l 2931
16.3%
r 2837
15.8%
m 2638
14.7%
o 2616
14.6%
N 2402
13.4%
i 291
 
1.6%
P 245
 
1.4%
t 245
 
1.4%
A 226
 
1.3%
Other values (8) 556
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2974
16.6%
l 2931
16.3%
r 2837
15.8%
m 2638
14.7%
o 2616
14.6%
N 2402
13.4%
i 291
 
1.6%
P 245
 
1.4%
t 245
 
1.4%
A 226
 
1.3%
Other values (8) 556
 
3.1%

SalePrice
Real number (ℝ)

MISSING 

Distinct663
Distinct (%)45.4%
Missing1459
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean180921.1959
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-10-24T14:28:48.004682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.50288
Coefficient of variation (CV)0.4391000319
Kurtosis6.53628186
Mean180921.1959
Median Absolute Deviation (MAD)38000
Skewness1.88287576
Sum264144946
Variance6311111264
MonotonicityNot monotonic
2024-10-24T14:28:48.157558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
0.7%
135000 17
 
0.6%
145000 14
 
0.5%
155000 14
 
0.5%
190000 13
 
0.4%
110000 13
 
0.4%
115000 12
 
0.4%
160000 12
 
0.4%
139000 11
 
0.4%
130000 11
 
0.4%
Other values (653) 1323
45.3%
(Missing) 1459
50.0%
ValueCountFrequency (%)
34900 1
< 0.1%
35311 1
< 0.1%
37900 1
< 0.1%
39300 1
< 0.1%
40000 1
< 0.1%
ValueCountFrequency (%)
755000 1
< 0.1%
745000 1
< 0.1%
625000 1
< 0.1%
611657 1
< 0.1%
582933 1
< 0.1%